Alzheimer's disease (AD) is the most common neurodegenerative disease in the U.S., affecting over 4 million people over the age of 65 years. Current medications treat the symptoms but not the underlying causes of disease. There is therefore an urgent need to understand the underlying pathogenic mechanisms of disease to enable rational drug design. During the last twenty years genetic studies of familial early onset AD have demonstrated that mutations in three genes cause AD via a common biochemical pathway involving Ass metabolism. Studies of late onset AD (LOAD) have implicated genotype at the apolipoprotein E (APOE) locus as a major risk factor that also acts via an Ass dependent mechanism. However, only 50% of LOAD cases carry a risk allele at the APOE locus. The goal of this study is to combine quantitative trait locus (QTL) studies of biochemical measures in cerebrospinal fluid and case control data to identify and validate novel genetic risk factors for LOAD. In the current proposal we will use publicly available existing genome-wide associate study (GWAS) data in case control datasets and newly acquired GWAS data (through the AD Genetics Consortium (ADGC)) in samples with biomarker measurements to identify SNPs/genes that influence risk for LOAD via an Ass dependent mechanism. GWAS data in the Washington University/University of Washington (WU/UW) biomarker datasets will be generated during the next twelve months as part of the first phase of genotyping by the ADGC. GWAS data for the AD Neuroimaging Initiative (ADNI) series and from several case control datasets are already in hand (approx. 3000 cases and 3000 controls). In this study we propose three specific aims focused on the analysis of this existing data. First we will test in the WU/UW CSF series for genetic factors on chromosome 10 and elsewhere in the genome that influence CSF Ass levels. Second, we will try to replicate these findings in an independent series with CSF biomarker measurements, collected by the ADNI consortium. We will then compare our findings in the CSF series with the results of a combined GWAS dataset in AD cases and controls. This will allow us to identify the putative AD genes that influence risk by an Ass dependent mechanism, facilitating follow-up mechanistic studies to confirm the functional effects of these genes on Ass metabolism and AD risk. The use of case-control and endophenotype measures in CSF provides a powerful and novel approach to the genetics of LOAD.